{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import zipfile\n",
    "import functools\n",
    "\n",
    "import rasterio\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['axes.grid'] = False\n",
    "mpl.rcParams['figure.figsize'] = (12,12)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.image as mpimg\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib as tfcontrib\n",
    "from tensorflow.python.keras import layers\n",
    "from tensorflow.python.keras import losses\n",
    "from tensorflow.python.keras import models\n",
    "from tensorflow.python.keras import backend as K\n",
    "\n",
    "config = tf.ConfigProto(log_device_placement=True)\n",
    "config.gpu_options.allow_growth = True\n",
    "\n",
    "session = tf.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice_coeff(y_true, y_pred):\n",
    "    smooth = 1.\n",
    "    # Flatten\n",
    "    y_true_f = tf.reshape(y_true, [-1])\n",
    "    y_pred_f = tf.reshape(y_pred, [-1])\n",
    "    intersection = tf.reduce_sum(y_true_f * y_pred_f)\n",
    "    score = (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice_loss(y_true, y_pred):\n",
    "    loss = 1 - dice_coeff(y_true, y_pred)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bce_dice_loss(y_true, y_pred):\n",
    "    loss = losses.binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model_path = '/home/mdj/Development/SAR-Ship-Detection/weights.hdf5'\n",
    "save_model_path = '/home/mdj/Development/SAR-Ship-Detection/weights_half_done_with_Clutter.hdf5'\n",
    "# Alternatively, load the weights directly: model.load_weights(save_model_path)\n",
    "model = models.load_model(save_model_path, custom_objects={'bce_dice_loss': bce_dice_loss,\n",
    "                                                           'dice_loss': dice_loss})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ais_filename = '/home/mdj/Datasets/ais/aisdk_20181006.csv'\n",
    "# sar_filename= '/home/mdj/S1A_IW_SLC__1SDV_20181006T054021_20181006T054048_024011_029F96_1702_Orb_Cal_deb_ML_Spk_TC.tif'\n",
    "\n",
    "ais_filename = '/home/mdj/Datasets/ais/aisdk_20180806.csv'\n",
    "sar_filename= '/home/mdj/S1A_IW_GRDH_1SDV_20180608T165254_20180608T165319_022268_0268E3_E9D4_Cal_TC.tif'\n",
    "\n",
    "\n",
    "img_win = (256, 256)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51269 21292\n",
      "+init=epsg:4326\n",
      "| 0.00, 0.00, 10.58|\n",
      "| 0.00,-0.00, 56.34|\n",
      "| 0.00, 0.00, 1.00|\n",
      "1\n",
      "(1,)\n"
     ]
    }
   ],
   "source": [
    "with rasterio.open(sar_filename) as sar_ds:\n",
    "    print(sar_ds.width, sar_ds.height)\n",
    "    print(sar_ds.crs)\n",
    "    print(sar_ds.transform)\n",
    "    print(sar_ds.count)\n",
    "    print(sar_ds.indexes)\n",
    "    band1 = sar_ds.read(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_image_at_coord(ds,lon,lat,width,height):\n",
    "    centoid = ds.index(lon, lat)\n",
    "    dx, dy = ds.res  \n",
    "    snip = band1[centoid[0]-int(height//2):centoid[0]+int(height//2), centoid[1]-int(width//2):centoid[1]+int(width//2)]\n",
    "    return snip, [centoid[0]-int(height//2),centoid[0]+int(height//2), centoid[1]-int(width//2), centoid[1]+int(width//2)]\n",
    "\n",
    "def extract_image_at_px(ds,x,y,width,height):\n",
    "    centoid = (x,y)\n",
    "    dx, dy = ds.res  \n",
    "    snip = band1[centoid[0]-int(height//2):centoid[0]+int(height//2), centoid[1]-int(width//2):centoid[1]+int(width//2)]\n",
    "    return snip, [centoid[0]-int(height//2),centoid[0]+int(height//2), centoid[1]-int(width//2), centoid[1]+int(width//2)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14574, -14318, -18947, -18691]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f141f360dd8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = (8.89347, 57.63367)\n",
    "# pos_time = datetime.datetime.strptime(\"2018-10-06 05:40:31:251 AM\", '%Y-%m-%d %I:%M:%S:%f %p')\n",
    "# print(pos_time)\n",
    "img, coords = extract_image_at_coord(sar_ds, pos[0], pos[1], 256, 256)\n",
    "print(coords)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im2arr =  np.expand_dims(np.transpose(np.array([img,img,img])), axis=0)\n",
    "# print(im2arr.shape)\n",
    "# Predicting the Test set results\n",
    "y_pred = model.predict(im2arr)[0]\n",
    "ytst = (256/np.max(np.transpose(y_pred[:,:,0]))*np.transpose(y_pred[:,:,0]))\n",
    "plt.imshow(np.transpose(y_pred[:,:,0]))\n",
    "imgc = plt.imshow(ytst,cmap='gray')\n",
    "# print(imgc.get_array().shape)\n",
    "# np.histogram(ytst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def latlon(ds, coord_base, pos):\n",
    "    x = coord_base[0] + pos[0]\n",
    "    y = coord_base[2] + pos[1]\n",
    "    return ds.xy(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.88353127088347, 57.63359607949027)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pimg = ytst\n",
    "\n",
    "# plt.hist(pimg.ravel(),256,[0.1,256]); plt.show()\n",
    "\n",
    "indices = np.where(ytst > 250)\n",
    "# print(np.mean(indices[0]), np.mean(indices[1][0]))\n",
    "\n",
    "\n",
    "# print(indices)\n",
    "# np.max(pimg)\n",
    "latlon(sar_ds, coords, (np.mean(indices[0]), np.mean(indices[1][0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "coordinates = []\n",
    "for x in range(256//2, sar_ds.shape[0], 256):\n",
    "    for y in range(256//2, sar_ds.shape[1], 256):\n",
    "        chip, coords = extract_image_at_px(sar_ds, x, y, 256, 256)\n",
    "        \n",
    "        if chip.shape != (256, 256):\n",
    "            continue\n",
    "            \n",
    "        im2arr =  np.expand_dims(np.transpose(np.array([chip,chip,chip])), axis=0)\n",
    "        y_pred = model.predict(im2arr)[0]\n",
    "        ytst = (256/np.max(np.transpose(y_pred[:,:,0]))*np.transpose(y_pred[:,:,0]))\n",
    "#         plt.imshow(np.transpose(y_pred[:,:,0]))\n",
    "#         imgc = plt.imshow(ytst,cmap='gray')\n",
    "        indices = np.where(ytst > 250)\n",
    "        # print(np.mean(indices[0]), np.mean(indices[1][0]))\n",
    "        if np.mean(indices[0]) > 0 and np.mean(indices[1]) > 0 and 254 not in indices[0] and 254 not in indices[1] and 1 not in indices[0] and 1 not in indices[1]:\n",
    "#             print(indices)\n",
    "            coordinates.append(latlon(sar_ds, coords, (np.mean(indices[0]), np.mean(indices[1][0]))))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"coord_predicted_tmp_oth.csv\", 'w') as fo:\n",
    "    fo.write(\"lon,lat\\n\")\n",
    "    for coord in coordinates:\n",
    "        fo.write(\"%f,%f\\n\" % coord)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter predictions outside EEZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from shapely.geometry import shape, Point\n",
    "# depending on your version, use: from shapely.geometry import shape, Point\n",
    "\n",
    "# load GeoJSON file containing sectors\n",
    "with open('../regions/eez_dk_full.geojson') as f:\n",
    "    js = json.load(f)\n",
    "\n",
    "# construct point based on lon/lat returned by geocoder\n",
    "\n",
    "# check each polygon to see if it contains the point\n",
    "with open(\"coord_predicted_tmp_oth_filtered.csv\", 'w') as fo:\n",
    "    fo.write(\"lon,lat\\n\")\n",
    "\n",
    "    for feature in js['features']:\n",
    "        polygon = shape(feature['geometry'])\n",
    "        for coord in coordinates:\n",
    "            point = Point(coord[0], coord[1])\n",
    "            if polygon.contains(point):\n",
    "#                 print ('Found containing polygon:', feature)\n",
    "                fo.write(\"%f,%f\\n\" % coord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
